Metamaterials can freely control terahertz waves to obtain the desired electromagnetic characteristics by designing the geometry and direction of the unit structure, which is widely used in sensing, communication and stealth technology in radar. The traditional design of terahertz metamaterial absorber usually requires continuous structural adjustment and a large number of simulations to meet the expected requirements. The process is heavily dependent on the experience of researchers, and the physical modeling and simulation solution process is time-consuming and inefficient, which has greatly hindered the development of metamaterial absorbers. Therefore, deep learning has been used to predict the structural parameters or spectra of metamaterial absorbers due to its powerful learning ability. However, when designing a new structure, a large number of training samples need to be reprepared, which is time-consuming and not universal. Particle swarm optimization can quickly converge to the optimal solution through the sharing and cooperation of individual information in the group, without prior preparation. Therefore, this paper proposed a fast design method of terahertz metamaterial absorber based on multi-objective particle swarm optimization algorithm. Taking a new center symmetric absorber structure composed of four L as an example, the structure parameters are optimized to achieve fast automatic design of metamaterial absorber. The multi-objective particle swarm optimization takes the absorptivity and quality factor as independent targets to design the structure parameters of the absorber, realizes the dual-objective optimization of the absorber, and overcomes the shortcoming of the multi-objective conflict that PSO cannot solve. The optimally-designed absorber achieves perfect absorption at 1.613THz with a quality factor of up to 319.72 and a sensing sensitivity of 264.5GHz/RIU when used for refractive index sensing. In addition, the causes of absorption peaks are analyzed in detail using impedance matching, surface current, and electric field distribution. By studying the polarization characteristics of the absorber, it is found that it is not sensitive to polarization, which is more stable in practical application. In summary, the multi-objective particle swarm optimization algorithm can realize the design according to the demand, reduce the experience requirement of researchers in the design of metamaterial absorber, improve the design efficiency and performance, and have great application potential in the design of terahertz functional devices.
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